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 Duration 21 hours

Course Outline

Introduction to AI for QA

  • What is Artificial Intelligence?
  • Machine Learning vs Deep Learning vs Rule-based Systems
  • The evolution of software testing driven by AI
  • Key advantages and challenges of AI in QA

Data and ML Basics for Testers

  • Understanding structured vs unstructured data
  • Features, labels, and training datasets
  • Supervised and unsupervised learning
  • Introduction to model evaluation (accuracy, precision, recall, etc.)
  • Real-world QA datasets

AI Use Cases in QA

  • AI-powered test case generation
  • Defect prediction using ML
  • Test prioritisation and risk-based testing
  • Visual testing with computer vision
  • Log analysis and anomaly detection
  • Natural language processing (NLP) for test scripts

AI Tools for QA

  • Overview of AI-enabled QA platforms
  • Utilising open-source libraries (e.g., Python, Scikit-learn, TensorFlow, Keras) for QA prototypes
  • Introduction to LLMs in test automation
  • Building a simple AI model to forecast test failures

Integrating AI into QA Workflows

  • Evaluating the AI-readiness of your QA processes
  • Continuous integration and AI: embedding intelligence into CI/CD pipelines
  • Designing intelligent test suites
  • Managing AI model drift and retraining cycles
  • Ethical considerations in AI-powered testing

Hands-on Labs and Capstone Project

  • Lab 1: Automate test case generation using AI
  • Lab 2: Build a defect prediction model using historical test data
  • Lab 3: Use an LLM to review and optimise test scripts
  • Capstone: End-to-end implementation of an AI-powered testing pipeline

Requirements

Participants should possess the following background:

  • Two or more years of experience in software testing or QA roles
  • Familiarity with test automation frameworks (e.g., Selenium, JUnit, Cypress)
  • Foundational programming knowledge, ideally in Python or JavaScript
  • Hands-on experience with version control and CI/CD tools (e.g., Git, Jenkins)
  • No prior experience in AI or ML is necessary, though a strong curiosity and willingness to experiment are vital

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